# RFID based Health Adherence Medicine Case Using Fair Federated Learning

arXiv: https://arxiv.org/abs/2408.11782 · HTML: https://mlsanity.com/paper/2408.11782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Fairness and Accuracy in Federated Learning](https://mlsanity.com/paper/2012.10069/) (2020)
- [AdaFed: Fair Federated Learning via Adaptive Common Descent Direction](https://mlsanity.com/paper/2401.04993/) (2024)

## Abstract (arXiv)

Medication nonadherence significantly reduces the effectiveness of therapies, yet it remains prevalent among patients. Nonadherence has been linked to adverse outcomes, including increased risks of mortality and hospitalization. Although various methods exist to help patients track medication schedules, such as the Intelligent Drug Administration System (IDAS) and Smart Blister, these tools often face challenges that hinder their commercial viability. Building on the principles of dosage measurement and information communication in IoT, we introduce the Smart Pill Case a smart health adherence tool that leverages RFID-based data recording and NFC-based data extraction. This system incorporates a load cell for precise dosage measurement and features an Android app to monitor medication intake, offer suggestions, and issue warnings. To enhance the effectiveness and personalization of the Smart Pill Case, we propose integrating federated learning into the system. Federated learning allows the Smart Pill Case to learn from medication adherence patterns across multiple users without compromising individual privacy. By training machine learning models on decentralized data collected from various Smart Pill Cases, the system can continuously improve its recommendations and warnings, adapting to the diverse needs and behaviors of users. This approach not only enhances the tools ability to support medication adherence but also ensures that sensitive user data remains secure and private.